Digital avatars are used in various digital applications, such as virtual environment platforms and social media services. A digital avatar is a virtual representation of a user. Examples of avatars include three-dimensional (“3D”) depictions of human beings or other objects. The various digital applications allow users to modify the appearance or behavior of their avatars. Examples of modifications to digital avatars include animating the avatars, applying different “clothing” graphics to the avatars (i.e., “dressing” and “re-dressing” the avatars), etc.
In some examples, avatars are generated by performing a 3D scan of a human being or other object. For instance, various scanning methods use depth sensors to generate a depth map of the human being or other object. The depth map is used to generate an avatar of a person. But disadvantages are associated with these existing solutions for generating digital avatars.
In one example, existing scanning methods treat the scanned human being as a single static object. In this single static object, no distinction is made between the person's clothing and the shape or pose (e.g. sitting, standing) of the person's body. Thus, these methods fail to provide information regarding the body shape or the body pose, and these methods also fail to distinguish between features of the person's body and the person's clothing. As a result, a virtual environment platform or other application is unable to estimate pose, body shape, or both for input images depicting complex clothing (e.g., loose-fitting clothing that does not conform to the shape of the body, clothing such as jackets or dresses that obscure the outline of the shoulders or the legs, etc.). Because the virtual environment platform cannot distinguish the clothing from the person's body and cannot accurately identify the person's body shape or pose, the virtual environment platform provides sub-optimal results when animating or re-dressing the digital avatar.
Even when existing solutions address the problem of the human being scanned as a single static object, these solutions present additional disadvantages. For example, if a virtual environment platform needs to recognize certain portions of the human body, a user that wishes to generate an avatar from a digital scan of the body must manually identify certain key portions of the body (e.g., knees, elbows, etc.). In another example, existing solutions may provide estimates of only the body shape of a scanned figure or only the pose of the scanned figure, but not both.
Thus, existing solutions for recognizing body shapes, body poses, and other characteristic in input images of human beings or other figures may present disadvantages for reasons such as (but not limited to) those described above.